C3D Features features are generated at 2 frames per second using a C3D network [1]} with temporal resolution of 16 frames.
Once again we train a frame level one-versus-rest SVM classifier for each activity class using a linear kernel.
The scoring of frame \(t\)  is defined by the resulting SVM scores: \(S^3_t = \lbrace {s_{1}^{3},...,s_{c}^{3},...s_{C}^{3}}\rbrace \) .
Finally, we perform mean pooling along the frames for each class to get another score vector \(S^3\) , which is used for video classification.
